Image-based branch detection and mapping for navigation

ABSTRACT

Navigation of an instrument within a luminal network can include image-based branch detection and mapping. Image-based branch detection can include identifying within an image one or more openings associated with one or more branches of a luminal network. Image-based branch mapping can include mapping the detected one or more openings to corresponding branches of the luminal network. Mapping may include comparing features of the openings to features of a set of expected openings. A position state estimate for the instrument can be determined from the mapped openings, which can facilitate navigation of the luminal network.

TECHNICAL FIELD

This disclosure relates generally to systems and methods for navigationof medical instruments, and more particularly to image-based branchdetection and mapping for navigation robotically-controlled medicalinstruments.

BACKGROUND

Medical procedures such as endoscopy (e.g., bronchoscopy) may involveaccessing and visualizing the inside of a patient's lumen (e.g.,airways) for diagnostic and/or therapeutic purposes. During a procedure,a flexible tubular tool or instrument, such as an endoscope, may beinserted into the patient's body. In some instances a second instrumentcan be passed through the endoscope to a tissue site identified fordiagnosis and/or treatment.

Bronchoscopy is a medical procedure that allows a physician to examinethe inside conditions of airways in a patient's lungs, such as bronchiand bronchioles. During the medical procedure, a thin, flexible tubulartool or instrument, known as a bronchoscope, may be inserted into thepatient's mouth and passed down the patient's throat into his or herlung airways towards a tissue site identified for subsequent diagnosisand treatment. The bronchoscope can have an interior lumen (a “workingchannel”) providing a pathway to the tissue site, and catheters andvarious medical tools can be inserted through the working channel to thetissue site.

In certain medical procedures, surgical robotic systems may be used tocontrol the insertion and/or manipulation of the surgical tools.Surgical robotic system may include at least one robotic arm or otherinstrument positioning device including a manipulator assembly used tocontrol the positioning of the surgical tool during the procedures.

SUMMARY

Robotically-enabled medical systems can be used to perform a variety ofmedical procedures, including both minimally invasive procedures, suchas laparoscopic procedures, and non-invasive procedures, such asendoscopic procedures. Among endoscopic procedures, robotically-enabledmedical systems can be used to perform bronchoscopy, ureteroscopy,gastroenterology, etc. During such procedures, a physician and/orcomputer system can navigate a medical instrument through a luminalnetwork of a patient. The luminal network can include a plurality ofbranched lumens (such as in bronchial or renal networks), or a singlelumen (such as a gastrointestinal tract). The robotically-enabledmedical systems can include navigation systems for guiding (or assistingwith the guidance of) the medical instrument through the luminalnetwork.

Embodiments of this disclosure relate to systems and techniques forimage-based branch detection and mapping. Image-based branch detectionand mapping may aid navigation through the luminal network. Image-basedbranch detection can include identifying, within an image captured withan imaging device on the instrument, one or more openings associatedwith one or more branches of a luminal network. Image-based branchmapping can include mapping the detected one or more openings tocorresponding branches of the luminal network. These systems andtechniques may be used to determine or estimate the position of aninstrument within the luminal network. The systems, methods and devicesof this disclosure each have several innovative aspects, no single oneof which is solely responsible for the desirable attributes disclosedherein.

Accordingly, a first aspect relates to a non-transitory computerreadable storage medium having stored thereon instructions that, whenexecuted, cause a processor of a device to at least: determine aposition state estimate of an instrument positioned within a currentbranch of a luminal network; determine a set of expected subsequentbranches based at least in part on the position state estimate and apreoperative model of the luminal network; capture an image of thecurrent branch with an imaging device positioned on the instrument;detect within the image a plurality of openings connecting subsequentbranches of the luminal network to the current branch; compare one ormore features of the detected plurality of openings to the set ofexpected subsequent branches to map each of the plurality of openings toone of the set of expected subsequent branches; and based at least inpart on the comparison, provide an updated position state estimate.

The first aspect may also comprise one or more of the following featuresin any combination: (a) wherein the updated position state estimatecomprises a probability that the position state estimate is correct; (b)wherein the probability is determined based in part on the comparisonbetween the one or more features of the detected plurality of openingsand the set of expected subsequent branches; (c) wherein the probabilityis determined based in part on the degree to which the one or morefeatures of the detected plurality of openings match the set of expectedsubsequent branches; (d) wherein the updated position state estimatecomprises an estimate of which subsequent branch the instrument will bemoved into; (e) wherein the instructions, when executed, cause theprocessor of the device to determine which opening of the plurality ofdetected openings is closer to a center of the image; (f) wherein theupdated position state estimate comprises a probability that theinstrument will be moved into the opening that is determined to becloser to the center of the image; (g) wherein the position stateestimate comprises an estimate of roll of the instrument about alongitudinal axis of the instrument, and wherein the updated positionstate estimate comprises a probability that the estimate of roll iscorrect, wherein the probability is determined by comparing anorientation of the detected openings within the image to an expectedorientation of the set of expected subsequent branches based on thepreoperative model; (h) wherein the position state estimate comprises anestimate of roll of the instrument about a longitudinal axis of theinstrument, and wherein the instructions, when executed, cause theprocessor of the device to determine a corrected estimate of roll bycomparing an orientation of the detected openings within the image to anexpected orientation of the set of expected subsequent branches based onthe preoperative model; (i) wherein the instructions, when executed,cause the processor of the device to determine the one or more featuresof the detected openings; (j) wherein the one or more features areselected from the group consisting of: a centroid of an opening, aprofile of an opening, and a vector connecting centroids of twoopenings; (k) wherein the instructions, when executed, cause theprocessor of the device to obtain information related to the set ofexpected subsequent branches from the preoperative model, wherein theinformation comprises at least one of centroid of an opening, a profileof an opening, and a vector connecting centroids of two openings; (l)wherein the instructions, when executed, cause the processor of thedevice to compare one or more features of the detected plurality ofopenings to the set of expected subsequent branches by: for each of thedetected openings, iteratively matching the one or more features of thedetected opening to the information related to the set of expectedsubsequent branches, wherein the highest match is used to map thedetected opening to the one of the expected subsequent branches; (m)wherein the instructions, when executed, cause the processor of thedevice to detect the plurality of openings within the image by:generating a histogram of pixel intensity values for the image;analyzing the histogram to identify the plurality of openings within theimage; and/or (n) wherein analyzing the histogram comprises: identifyingat least two peaks within the histogram; identifying a midpoint betweenthe at least two peaks; categorizing pixels on a first side of themidpoint as openings.

A second aspect relates to a robotic system for navigating a luminalnetwork of a patient, the robotic system comprising: an instrumenthaving an elongate body configured to be inserted into the luminalnetwork, and an imaging device positioned on a distal portion of theelongate body; an instrument positioning device attached to theinstrument, the instrument positioning device configured to move theinstrument through the luminal network; at least one computer-readablememory having stored thereon executable instructions; and one or moreprocessors in communication with the at least one computer-readablememory and configured to execute the instructions to cause the system toat least: determine a position state estimate of the instrumentpositioned within a current branch of a luminal network; determine a setof expected subsequent branches based at least in part on the initialstate estimate and a preoperative model of the luminal network; capturean image of the current branch of the luminal network with an imagingdevice positioned on the instrument; detect within the image a pluralityof openings connecting subsequent branches of the luminal network to thecurrent branch; compare features of the detected plurality of openingsto the set of expected subsequent branches to map each of the pluralityof openings to one of the expected subsequent branches; and based atleast in part on the comparison, provide an updated position stateestimate.

The second aspect may also comprise one or more of the followingfeatures in any combination: (a) wherein the instrument comprises anendoscope; (b) wherein the instrument positioning device comprises arobotic arm; (c) wherein the luminal network comprises a bronchialnetwork of a lung, a gastrointestinal tract, or a renal network of akidney; (d) wherein the updated position state estimate comprises aprobability that the position state estimate is correct; (e) theprobability is determined based in part on the comparison between thefeatures of the detected plurality of openings and the set of expectedsubsequent branches; (f) wherein the probability is determined based inpart on the degree to which the features of the detected plurality ofopenings match the set of expected subsequent branches; (g) wherein theupdated position state estimate comprises an estimate of whichsubsequent branch the instrument will be moved into; (h) wherein theinstructions, when executed, cause the one or more processors todetermine which opening of the plurality of detected openings is closerto a center of the image; (i) wherein the updated position stateestimate comprises a probability that the instrument will be moved intothe opening that is determined to be closer to the center of the image;(j) wherein the position state estimate comprises an estimate of roll ofthe instrument about a longitudinal axis of the instrument, and whereinthe updated position state estimate comprises a probability that theestimate of roll is correct, wherein the probability is determined bycomparing an orientation of the detected openings within the image to anexpected orientation of the set of expected subsequent branches based onthe preoperative model; (k) wherein the position state estimatecomprises an estimate of roll of the instrument about a longitudinalaxis of the instrument, and wherein the instructions, when executed,cause the one or more processors to determine a corrected estimate ofroll by comparing an orientation of the detected openings within theimage to an expected orientation of the set of expected subsequentbranches based on the preoperative model; (l) wherein the instructions,when executed, cause the one or more processors to determine the one ormore features of the detected openings; (m) the one or more features areselected from the group consisting of: a centroid of an opening, aprofile of an opening, and a vector connecting centroids of twoopenings; (n) wherein the instructions, when executed, cause the one ormore processors of the device to obtain information related to the setof expected subsequent branches from the preoperative model, wherein theinformation comprises at least one of centroid of an opening, a profileof an opening, and a vector connecting centroids of two openings; (o)wherein the instructions, when executed, cause the one or moreprocessors to compare one or more features of the detected plurality ofopenings to the set of expected subsequent branches by: for each of thedetected openings, iteratively matching the one or more features of thedetected opening to the information related to the set of expectedsubsequent branches, wherein the highest match is used to map thedetected opening to the one of the expected subsequent branches; (p)wherein the instructions, when executed, cause the one or moreprocessors to detect the plurality of openings within the image by:generating a histogram of pixel intensity values for the image;analyzing the histogram to identify the plurality of openings within theimage; and/or (q) wherein analyzing the histogram comprises: identifyingat least two peaks within the histogram; identifying a midpoint betweenthe at least two peaks; and categorizing pixels on a first side of themidpoint as openings.

A third aspect relates to a method for navigating a luminal network, themethod comprising: inserting an instrument into a current branch of theluminal network; receiving a position state estimate for the instrument;determining a set of expected subsequent branches based at least in parton the initial state estimate and a preoperative model of the luminalnetwork; capturing an image of the current branch with an imaging devicepositioned on the instrument; analyzing the image to detect a pluralityof openings connecting subsequent branches to the current branch;comparing features of the detected plurality of openings to the set ofexpected subsequent branches to map each of the plurality of openings toone of the expected subsequent branches; and based at least in part onthe comparison, provide an updated position state estimate.

The third aspect may also comprise one or more of the following featuresin any combination: (a) wherein the instrument comprises an endoscope;(b) wherein the instrument positioning devices comprises a robotic arm;(c) wherein the luminal network comprises a bronchial network of a lung,a gastrointestinal tract, or a renal network of a kidney; (d) whereinthe updated position state estimate comprises a probability that theposition state estimate is correct; (e) wherein the probability isdetermined based in part on the comparison between the one or morefeatures of the detected plurality of openings and the set of expectedsubsequent branches; (f) wherein the probability is determined based inpart on the degree to which the one or more features of the detectedplurality of openings match the set of expected subsequent branches; (g)wherein the updated position state estimate comprises an estimate ofwhich subsequent branch the instrument will be moved into; (h) whereinfurther comprising determine which opening of the plurality of detectedopenings is closer to a center of the image; (i) wherein the updatedposition state estimate comprises a probability that the instrument willbe moved into the opening that is determined to be closer to the centerof the image; (j) wherein the position state estimate comprises anestimate of roll of the instrument about a longitudinal axis of theinstrument, and wherein the updated position state estimate comprises aprobability that the estimate of roll is correct, the method furthercomprising: comparing an orientation of the detected openings within theimage to an expected orientation of the set of expected subsequentbranches based on the preoperative model to determine the probability;(k) wherein the position state estimate comprises an estimate of roll ofthe instrument about a longitudinal axis of the instrument, and whereinthe method further comprises: determining a corrected estimate of rollby comparing an orientation of the detected openings within the image toan expected orientation of the set of expected subsequent branches basedon the preoperative model; (l) determining the one or more features ofthe detected openings; (m) wherein the one or more features are selectedfrom the group consisting of: a centroid of an opening, a profile of anopening, and a vector connecting centroids of two openings; (n)obtaining information related to the set of expected subsequent branchesfrom the preoperative model, wherein the information comprises at leastone of centroid of an opening, a profile of an opening, and a vectorconnecting centroids of two openings; (o) wherein comparing features ofthe detected plurality of openings to the set of expected subsequentbranches comprises: for each of the detected openings, iterativelymatching the one or more features of the detected opening to theinformation related to the set of expected subsequent branches, whereinthe highest match is used to map the detected opening to the one of theexpected subsequent branches; (p) wherein detecting the plurality ofopenings within the image comprises: generating a histogram of pixelintensity values for the image; and analyzing the histogram to identifythe plurality of openings within the image; and/or (q) wherein analyzingthe histogram comprises: identifying at least two peaks within thehistogram; identifying a midpoint between the at least two peaks;categorizing pixels on a first side of the midpoint as openings.

A fourth aspect relates to a method for identifying openings of branchesof a luminal network, the method comprising: capturing an image of aninterior a branch of a luminal network with an imaging device positionedwithin the branch; generating a histogram of pixel intensity values forthe image; and identifying pixels below a threshold value as indicatingopenings within the image.

The fourth aspect may also include one or more of the following featuresin any combination: (a) determining the threshold value based on thehistogram; (b) wherein determining the threshold value comprises:identifying at least two peaks within the histogram; identifying amidpoint between the at least two peaks; and setting the threshold valueequal to the intensity value of the midpoint; (c) for each of theidentified openings within the image, determine a centroid of theopening; (d) for each of the identified openings within the image,determine a profile of the opening; (e) comparing a number of theidentified openings in the image to a bad frame detector threshold; andif the number of the identified openings exceeds the bad frame detectorthreshold: capturing a second image of the interior of the branch; andanalyzing the second image to determine openings within the secondimage; (f) wherein the luminal network is a bronchial network of a lung,a gastrointestinal tract, or a renal network of a kidney.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe appended drawings, provided to illustrate and not to limit thedisclosed aspects, wherein like designations denote like elements.

FIG. 1 illustrates an embodiment of a cart-based robotic system arrangedfor diagnostic and/or therapeutic bronchoscopy procedure(s).

FIG. 2 depicts further aspects of the robotic system of FIG. 1.

FIG. 3 illustrates an embodiment of the robotic system of FIG. 1arranged for ureteroscopy.

FIG. 4 illustrates an embodiment of the robotic system of FIG. 1arranged for a vascular procedure.

FIG. 5 illustrates an embodiment of a table-based robotic systemarranged for a bronchoscopy procedure.

FIG. 6 provides an alternative view of the robotic system of FIG. 5.

FIG. 7 illustrates an example system configured to stow robotic arm(s).

FIG. 8 illustrates an embodiment of a table-based robotic systemconfigured for a ureteroscopy procedure.

FIG. 9 illustrates an embodiment of a table-based robotic systemconfigured for a laparoscopic procedure.

FIG. 10 illustrates an embodiment of the table-based robotic system ofFIGS. 5-9 with pitch or tilt adjustment.

FIG. 11 provides a detailed illustration of the interface between thetable and the column of the table-based robotic system of FIGS. 5-10.

FIG. 12 illustrates an exemplary instrument driver.

FIG. 13 illustrates an exemplary medical instrument with a pairedinstrument driver.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument.

FIG. 15 depicts a block diagram illustrating a localization system thatestimates a location of one or more elements of the robotic systems ofFIGS. 1-10, such as the location of the instrument of FIGS. 13-14, inaccordance to an example embodiment.

FIG. 16 illustrates an example of an instrument navigating a luminalnetwork.

FIG. 17 illustrates an example command console for arobotically-controlled surgical system.

FIG. 18 illustrates a distal end of an embodiment of a medicalinstrument.

FIG. 19 depicts a flowchart illustrating an example method forimage-based branch detection and mapping.

FIG. 20 illustrates an example image of an interior of a branch of aluminal network.

FIG. 21 depicts a flowchart illustrating an example method forimage-based branch detection.

FIG. 22 illustrates an example histogram of pixel intensity values.

FIGS. 23A and 23B illustrate example images illustrating image-basedbranch detection.

FIG. 24 depicts a flowchart illustrating an example method forimage-based branch detection.

FIG. 25 illustrates a simplified view of a luminal network.

FIG. 26 depicts a flowchart illustrating an example method forimage-based branch mapping.

FIGS. 27A-27C illustrate example steps in a method for image-basedbranch mapping.

FIG. 28 illustrates an example image illustrating image-based branchprediction.

FIG. 29 depicts a flowchart illustrating an example method forimage-based branch detection and mapping.

DETAILED DESCRIPTION

1. Overview.

Aspects of the present disclosure may be integrated into arobotically-enabled medical system capable of performing a variety ofmedical procedures, including both minimally invasive, such aslaparoscopy, and non-invasive, such as endoscopy, procedures. Amongendoscopy procedures, the system may be capable of performingbronchoscopy, ureteroscopy, gastroenterology, etc.

In addition to performing the breadth of procedures, the system mayprovide additional benefits, such as enhanced imaging and guidance toassist the physician. Additionally, the system may provide the physicianwith the ability to perform the procedure from an ergonomic positionwithout the need for awkward arm motions and positions. Still further,the system may provide the physician with the ability to perform theprocedure with improved ease of use such that one or more of theinstruments of the system can be controlled by a single user.

Various embodiments will be described below in conjunction with thedrawings for purposes of illustration. It should be appreciated thatmany other implementations of the disclosed concepts are possible, andvarious advantages can be achieved with the disclosed implementations.Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

A. Robotic System—Cart.

The robotically-enabled medical system may be configured in a variety ofways depending on the particular procedure. FIG. 1 illustrates anembodiment of a cart-based robotically-enabled system 10 arranged for adiagnostic and/or therapeutic bronchoscopy procedure. During abronchoscopy, the system 10 may comprise a cart 11 having one or morerobotic arms 12 to deliver a medical instrument, such as a steerableendoscope 13, which may be a procedure-specific bronchoscope forbronchoscopy, to a natural orifice access point (i.e., the mouth of thepatient positioned on a table in the present example) to deliverdiagnostic and/or therapeutic tools. As shown, the cart 11 may bepositioned proximate to the patient's upper torso in order to provideaccess to the access point. Similarly, the robotic arms 12 may beactuated to position the bronchoscope relative to the access point. Thearrangement in FIG. 1 may also be utilized when performing agastro-intestinal (GI) procedure with a gastroscope, a specializedendoscope for GI procedures. FIG. 2 depicts an example embodiment of thecart in greater detail.

With continued reference to FIG. 1, once the cart 11 is properlypositioned, the robotic arms 12 may insert the steerable endoscope 13into the patient robotically, manually, or a combination thereof. Asshown, the steerable endoscope 13 may comprise at least two telescopingparts, such as an inner leader portion and an outer sheath portion, eachportion coupled to a separate instrument driver from the set ofinstrument drivers 28, each instrument driver coupled to the distal endof an individual robotic arm. This linear arrangement of the instrumentdrivers 28, which facilitates coaxially aligning the leader portion withthe sheath portion, creates a “virtual rail” 29 that may be repositionedin space by manipulating the one or more robotic arms 12 into differentangles and/or positions. The virtual rails described herein are depictedin the Figures using dashed lines, and accordingly the dashed lines donot depict any physical structure of the system. Translation of theinstrument drivers 28 along the virtual rail 29 telescopes the innerleader portion relative to the outer sheath portion or advances orretracts the endoscope 13 from the patient. The angle of the virtualrail 29 may be adjusted, translated, and pivoted based on clinicalapplication or physician preference. For example, in bronchoscopy, theangle and position of the virtual rail 29 as shown represents acompromise between providing physician access to the endoscope 13 whileminimizing friction that results from bending the endoscope 13 into thepatient's mouth.

The endoscope 13 may be directed down the patient's trachea and lungsafter insertion using precise commands from the robotic system untilreaching the target destination or operative site. In order to enhancenavigation through the patient's lung network and/or reach the desiredtarget, the endoscope 13 may be manipulated to telescopically extend theinner leader portion from the outer sheath portion to obtain enhancedarticulation and greater bend radius. The use of separate instrumentdrivers 28 also allows the leader portion and sheath portion to bedriven independent of each other.

For example, the endoscope 13 may be directed to deliver a biopsy needleto a target, such as, for example, a lesion or nodule within the lungsof a patient. The needle may be deployed down a working channel thatruns the length of the endoscope to obtain a tissue sample to beanalyzed by a pathologist. Depending on the pathology results,additional tools may be deployed down the working channel of theendoscope for additional biopsies. After identifying a nodule to bemalignant, the endoscope 13 may endoscopically deliver tools to resectthe potentially cancerous tissue. In some instances, diagnostic andtherapeutic treatments may need to be delivered in separate procedures.In those circumstances, the endoscope 13 may also be used to deliver afiducial to “mark” the location of the target nodule as well. In otherinstances, diagnostic and therapeutic treatments may be delivered duringthe same procedure.

The system 10 may also include a movable tower 30, which may beconnected via support cables to the cart 11 to provide support forcontrols, electronics, fluidics, optics, sensors, and/or power to thecart 11. Placing such functionality in the tower 30 allows for a smallerform factor cart 11 that may be more easily adjusted and/orre-positioned by an operating physician and his/her staff. Additionally,the division of functionality between the cart/table and the supporttower 30 reduces operating room clutter and facilitates improvingclinical workflow. While the cart 11 may be positioned close to thepatient, the tower 30 may be stowed in a remote location to stay out ofthe way during a procedure.

In support of the robotic systems described above, the tower 30 mayinclude component(s) of a computer-based control system that storescomputer program instructions, for example, within a non-transitorycomputer-readable storage medium such as a persistent magnetic storagedrive, solid state drive, etc. The execution of those instructions,whether the execution occurs in the tower 30 or the cart 11, may controlthe entire system or sub-system(s) thereof. For example, when executedby a processor of the computer system, the instructions may cause thecomponents of the robotics system to actuate the relevant carriages andarm mounts, actuate the robotics arms, and control the medicalinstruments. For example, in response to receiving the control signal,the motors in the joints of the robotics arms may position the arms intoa certain posture.

The tower 30 may also include a pump, flow meter, valve control, and/orfluid access in order to provide controlled irrigation and aspirationcapabilities to system that may be deployed through the endoscope 13.These components may also be controlled using the computer system oftower 30. In some embodiments, irrigation and aspiration capabilitiesmay be delivered directly to the endoscope 13 through separate cable(s).

The tower 30 may include a voltage and surge protector designed toprovide filtered and protected electrical power to the cart 11, therebyavoiding placement of a power transformer and other auxiliary powercomponents in the cart 11, resulting in a smaller, more moveable cart11.

The tower 30 may also include support equipment for the sensors deployedthroughout the robotic system 10. For example, the tower 30 may includeopto-electronics equipment for detecting, receiving, and processing datareceived from the optical sensors or cameras throughout the roboticsystem 10. In combination with the control system, such opto-electronicsequipment may be used to generate real-time images for display in anynumber of consoles deployed throughout the system, including in thetower 30. Similarly, the tower 30 may also include an electronicsubsystem for receiving and processing signals received from deployedelectromagnetic (EM) sensors. The tower 30 may also be used to house andposition an EM field generator for detection by EM sensors in or on themedical instrument.

The tower 30 may also include a console 31 in addition to other consolesavailable in the rest of the system, e.g., console mounted on top of thecart. The console 31 may include a user interface and a display screen,such as a touchscreen, for the physician operator. Consoles in system 10are generally designed to provide both robotic controls as well aspre-operative and real-time information of the procedure, such asnavigational and localization information of the endoscope 13. When theconsole 31 is not the only console available to the physician, it may beused by a second operator, such as a nurse, to monitor the health orvitals of the patient and the operation of system, as well as provideprocedure-specific data, such as navigational and localizationinformation.

The tower 30 may be coupled to the cart 11 and endoscope 13 through oneor more cables or connections (not shown). In some embodiments, thesupport functionality from the tower 30 may be provided through a singlecable to the cart 11, simplifying and de-cluttering the operating room.In other embodiments, specific functionality may be coupled in separatecabling and connections. For example, while power may be providedthrough a single power cable to the cart, the support for controls,optics, fluidics, and/or navigation may be provided through a separatecable.

FIG. 2 provides a detailed illustration of an embodiment of the cartfrom the cart-based robotically-enabled system shown in FIG. 1. The cart11 generally includes an elongated support structure 14 (often referredto as a “column”), a cart base 15, and a console 16 at the top of thecolumn 14. The column 14 may include one or more carriages, such as acarriage 17 (alternatively “arm support”) for supporting the deploymentof one or more robotic arms 12 (three shown in FIG. 2). The carriage 17may include individually configurable arm mounts that rotate along aperpendicular axis to adjust the base of the robotic arms 12 for betterpositioning relative to the patient. The carriage 17 also includes acarriage interface 19 that allows the carriage 17 to verticallytranslate along the column 14.

The carriage interface 19 is connected to the column 14 through slots,such as slot 20, that are positioned on opposite sides of the column 14to guide the vertical translation of the carriage 17. The slot 20contains a vertical translation interface to position and hold thecarriage at various vertical heights relative to the cart base 15.Vertical translation of the carriage 17 allows the cart 11 to adjust thereach of the robotic arms 12 to meet a variety of table heights, patientsizes, and physician preferences. Similarly, the individuallyconfigurable arm mounts on the carriage 17 allow the robotic arm base 21of robotic arms 12 to be angled in a variety of configurations.

In some embodiments, the slot 20 may be supplemented with slot coversthat are flush and parallel to the slot surface to prevent dirt andfluid ingress into the internal chambers of the column 14 and thevertical translation interface as the carriage 17 vertically translates.The slot covers may be deployed through pairs of spring spoolspositioned near the vertical top and bottom of the slot 20. The coversare coiled within the spools until deployed to extend and retract fromtheir coiled state as the carriage 17 vertically translates up and down.The spring-loading of the spools provides force to retract the coverinto a spool when carriage 17 translates towards the spool, while alsomaintaining a tight seal when the carriage 17 translates away from thespool. The covers may be connected to the carriage 17 using, forexample, brackets in the carriage interface 19 to ensure properextension and retraction of the cover as the carriage 17 translates.

The column 14 may internally comprise mechanisms, such as gears andmotors, that are designed to use a vertically aligned lead screw totranslate the carriage 17 in a mechanized fashion in response to controlsignals generated in response to user inputs, e.g., inputs from theconsole 16.

The robotic arms 12 may generally comprise robotic arm bases 21 and endeffectors 22, separated by a series of linkages 23 that are connected bya series of joints 24, each joint comprising an independent actuator,each actuator comprising an independently controllable motor. Eachindependently controllable joint represents an independent degree offreedom available to the robotic arm. Each of the arms 12 have sevenjoints, and thus provide seven degrees of freedom. A multitude of jointsresult in a multitude of degrees of freedom, allowing for “redundant”degrees of freedom. Redundant degrees of freedom allow the robotic arms12 to position their respective end effectors 22 at a specific position,orientation, and trajectory in space using different linkage positionsand joint angles. This allows for the system to position and direct amedical instrument from a desired point in space while allowing thephysician to move the arm joints into a clinically advantageous positionaway from the patient to create greater access, while avoiding armcollisions.

The cart base 15 balances the weight of the column 14, carriage 17, andarms 12 over the floor. Accordingly, the cart base 15 houses heaviercomponents, such as electronics, motors, power supply, as well ascomponents that either enable movement and/or immobilize the cart. Forexample, the cart base 15 includes rollable wheel-shaped casters 25 thatallow for the cart to easily move around the room prior to a procedure.After reaching the appropriate position, the casters 25 may beimmobilized using wheel locks to hold the cart 11 in place during theprocedure.

Positioned at the vertical end of column 14, the console 16 allows forboth a user interface for receiving user input and a display screen (ora dual-purpose device such as, for example, a touchscreen 26) to providethe physician user with both pre-operative and intra-operative data.Potential pre-operative data on the touchscreen 26 may includepre-operative plans, navigation and mapping data derived frompre-operative computerized tomography (CT) scans, and/or notes frompre-operative patient interviews. Intra-operative data on display mayinclude optical information provided from the tool, sensor andcoordinate information from sensors, as well as vital patientstatistics, such as respiration, heart rate, and/or pulse. The console16 may be positioned and tilted to allow a physician to access theconsole from the side of the column 14 opposite carriage 17. From thisposition the physician may view the console 16, robotic arms 12, andpatient while operating the console 16 from behind the cart 11. Asshown, the console 16 also includes a handle 27 to assist withmaneuvering and stabilizing cart 11.

FIG. 3 illustrates an embodiment of a robotically-enabled system 10arranged for ureteroscopy. In a ureteroscopic procedure, the cart 11 maybe positioned to deliver a ureteroscope 32, a procedure-specificendoscope designed to traverse a patient's urethra and ureter, to thelower abdominal area of the patient. In a ureteroscopy, it may bedesirable for the ureteroscope 32 to be directly aligned with thepatient's urethra to reduce friction and forces on the sensitive anatomyin the area. As shown, the cart 11 may be aligned at the foot of thetable to allow the robotic arms 12 to position the ureteroscope 32 fordirect linear access to the patient's urethra. From the foot of thetable, the robotic arms 12 may insert ureteroscope 32 along the virtualrail 33 directly into the patient's lower abdomen through the urethra.

After insertion into the urethra, using similar control techniques as inbronchoscopy, the ureteroscope 32 may be navigated into the bladder,ureters, and/or kidneys for diagnostic and/or therapeutic applications.For example, the ureteroscope 32 may be directed into the ureter andkidneys to break up kidney stone build up using laser or ultrasoniclithotripsy device deployed down the working channel of the ureteroscope32. After lithotripsy is complete, the resulting stone fragments may beremoved using baskets deployed down the ureteroscope 32.

FIG. 4 illustrates an embodiment of a robotically-enabled systemsimilarly arranged for a vascular procedure. In a vascular procedure,the system 10 may be configured such the cart 11 may deliver a medicalinstrument 34, such as a steerable catheter, to an access point in thefemoral artery in the patient's leg. The femoral artery presents both alarger diameter for navigation as well as relatively less circuitous andtortuous path to the patient's heart, which simplifies navigation. As ina ureteroscopic procedure, the cart 11 may be positioned towards thepatient's legs and lower abdomen to allow the robotic arms 12 to providea virtual rail 35 with direct linear access to the femoral artery accesspoint in the patient's thigh/hip region. After insertion into theartery, the medical instrument 34 may be directed and inserted bytranslating the instrument drivers 28. Alternatively, the cart may bepositioned around the patient's upper abdomen in order to reachalternative vascular access points, such as, for example, the carotidand brachial arteries near the shoulder and wrist.

B. Robotic System—Table.

Embodiments of the robotically-enabled medical system may alsoincorporate the patient's table. Incorporation of the table reduces theamount of capital equipment within the operating room by removing thecart, which allows greater access to the patient. FIG. 5 illustrates anembodiment of such a robotically-enabled system arranged for abronchoscopy procedure. System 36 includes a support structure or column37 for supporting platform 38 (shown as a “table” or “bed”) over thefloor. Much like in the cart-based systems, the end effectors of therobotic arms 39 of the system 36 comprise instrument drivers 42 that aredesigned to manipulate an elongated medical instrument, such as abronchoscope 40 in FIG. 5, through or along a virtual rail 41 formedfrom the linear alignment of the instrument drivers 42. In practice, aC-arm for providing fluoroscopic imaging may be positioned over thepatient's upper abdominal area by placing the emitter and detectoraround table 38.

FIG. 6 provides an alternative view of the system 36 without the patientand medical instrument for discussion purposes. As shown, the column 37may include one or more carriages 43 shown as ring-shaped in the system36, from which the one or more robotic arms 39 may be based. Thecarriages 43 may translate along a vertical column interface 44 thatruns the length of the column 37 to provide different vantage pointsfrom which the robotic arms 39 may be positioned to reach the patient.The carriage(s) 43 may rotate around the column 37 using a mechanicalmotor positioned within the column 37 to allow the robotic arms 39 tohave access to multiples sides of the table 38, such as, for example,both sides of the patient. In embodiments with multiple carriages, thecarriages may be individually positioned on the column and may translateand/or rotate independent of the other carriages. While carriages 43need not surround the column 37 or even be circular, the ring-shape asshown facilitates rotation of the carriages 43 around the column 37while maintaining structural balance. Rotation and translation of thecarriages 43 allows the system to align the medical instruments, such asendoscopes and laparoscopes, into different access points on thepatient.

The arms 39 may be mounted on the carriages through a set of arm mounts45 comprising a series of joints that may individually rotate and/ortelescopically extend to provide additional configurability to therobotic arms 39. Additionally, the arm mounts 45 may be positioned onthe carriages 43 such that, when the carriages 43 are appropriatelyrotated, the arm mounts 45 may be positioned on either the same side oftable 38 (as shown in FIG. 6), on opposite sides of table 38 (as shownin FIG. 9), or on adjacent sides of the table 38 (not shown).

The column 37 structurally provides support for the table 38, and a pathfor vertical translation of the carriages. Internally, the column 37 maybe equipped with lead screws for guiding vertical translation of thecarriages, and motors to mechanize the translation of said carriagesbased the lead screws. The column 37 may also convey power and controlsignals to the carriage 43 and robotic arms 39 mounted thereon.

The table base 46 serves a similar function as the cart base 15 in cart11 shown in FIG. 2, housing heavier components to balance the table/bed38, the column 37, the carriages 43, and the robotic arms 39. The tablebase 46 may also incorporate rigid casters to provide stability duringprocedures. Deployed from the bottom of the table base 46, the castersmay extend in opposite directions on both sides of the base 46 andretract when the system 36 needs to be moved.

Continuing with FIG. 6, the system 36 may also include a tower (notshown) that divides the functionality of system 36 between table andtower to reduce the form factor and bulk of the table. As in earlierdisclosed embodiments, the tower may be provide a variety of supportfunctionalities to table, such as processing, computing, and controlcapabilities, power, fluidics, and/or optical and sensor processing. Thetower may also be movable to be positioned away from the patient toimprove physician access and de-clutter the operating room.Additionally, placing components in the tower allows for more storagespace in the table base for potential stowage of the robotic arms. Thetower may also include a console that provides both a user interface foruser input, such as keyboard and/or pendant, as well as a display screen(or touchscreen) for pre-operative and intra-operative information, suchas real-time imaging, navigation, and tracking information.

In some embodiments, a table base may stow and store the robotic armswhen not in use. FIG. 7 illustrates a system 47 that stows robotic armsin an embodiment of the table-based system. In system 47, carriages 48may be vertically translated into base 49 to stow robotic arms 50, armmounts 51, and the carriages 48 within the base 49. Base covers 52 maybe translated and retracted open to deploy the carriages 48, arm mounts51, and arms 50 around column 53, and closed to stow to protect themwhen not in use. The base covers 52 may be sealed with a membrane 54along the edges of its opening to prevent dirt and fluid ingress whenclosed.

FIG. 8 illustrates an embodiment of a robotically-enabled table-basedsystem configured for a ureteroscopy procedure. In a ureteroscopy, thetable 38 may include a swivel portion 55 for positioning a patientoff-angle from the column 37 and table base 46. The swivel portion 55may rotate or pivot around a pivot point (e.g., located below thepatient's head) in order to position the bottom portion of the swivelportion 55 away from the column 37. For example, the pivoting of theswivel portion 55 allows a C-arm (not shown) to be positioned over thepatient's lower abdomen without competing for space with the column (notshown) below table 38. By rotating the carriage 35 (not shown) aroundthe column 37, the robotic arms 39 may directly insert a ureteroscope 56along a virtual rail 57 into the patient's groin area to reach theurethra. In a ureteroscopy, stirrups 58 may also be fixed to the swivelportion 55 of the table 38 to support the position of the patient's legsduring the procedure and allow clear access to the patient's groin area.

In a laparoscopic procedure, through small incision(s) in the patient'sabdominal wall, minimally invasive instruments (elongated in shape toaccommodate the size of the one or more incisions) may be inserted intothe patient's anatomy. After inflation of the patient's abdominalcavity, the instruments, often referred to as laparoscopes, may bedirected to perform surgical tasks, such as grasping, cutting, ablating,suturing, etc. FIG. 9 illustrates an embodiment of a robotically-enabledtable-based system configured for a laparoscopic procedure. As shown inFIG. 9, the carriages 43 of the system 36 may be rotated and verticallyadjusted to position pairs of the robotic arms 39 on opposite sides ofthe table 38, such that laparoscopes 59 may be positioned using the armmounts 45 to be passed through minimal incisions on both sides of thepatient to reach his/her abdominal cavity.

To accommodate laparoscopic procedures, the robotically-enabled tablesystem may also tilt the platform to a desired angle. FIG. 10illustrates an embodiment of the robotically-enabled medical system withpitch or tilt adjustment. As shown in FIG. 10, the system 36 mayaccommodate tilt of the table 38 to position one portion of the table ata greater distance from the floor than the other. Additionally, the armmounts 45 may rotate to match the tilt such that the arms 39 maintainthe same planar relationship with table 38. To accommodate steeperangles, the column 37 may also include telescoping portions 60 thatallow vertical extension of column 37 to keep the table 38 from touchingthe floor or colliding with base 46.

FIG. 11 provides a detailed illustration of the interface between thetable 38 and the column 37. Pitch rotation mechanism 61 may beconfigured to alter the pitch angle of the table 38 relative to thecolumn 37 in multiple degrees of freedom. The pitch rotation mechanism61 may be enabled by the positioning of orthogonal axes 1, 2 at thecolumn-table interface, each axis actuated by a separate motor 3, 4responsive to an electrical pitch angle command. Rotation along onescrew 5 would enable tilt adjustments in one axis 1, while rotationalong the other screw 6 would enable tilt adjustments along the otheraxis 2.

For example, pitch adjustments are particularly useful when trying toposition the table in a Trendelenburg position, i.e., position thepatient's lower abdomen at a higher position from the floor than thepatient's lower abdomen, for lower abdominal surgery. The Trendelenburgposition causes the patient's internal organs to slide towards his/herupper abdomen through the force of gravity, clearing out the abdominalcavity for minimally invasive tools to enter and perform lower abdominalsurgical procedures, such as laparoscopic prostatectomy.

C. Instrument Driver & Interface.

The end effectors of the system's robotic arms comprise (i) aninstrument driver (alternatively referred to as “instrument drivemechanism” or “instrument device manipulator”) that incorporateelectro-mechanical means for actuating the medical instrument and (ii) aremovable or detachable medical instrument which may be devoid of anyelectro-mechanical components, such as motors. This dichotomy may bedriven by the need to sterilize medical instruments used in medicalprocedures, and the inability to adequately sterilize expensive capitalequipment due to their intricate mechanical assemblies and sensitiveelectronics. Accordingly, the medical instruments may be designed to bedetached, removed, and interchanged from the instrument driver (and thusthe system) for individual sterilization or disposal by the physician orthe physician's staff. In contrast, the instrument drivers need not bechanged or sterilized, and may be draped for protection.

FIG. 12 illustrates an example instrument driver. Positioned at thedistal end of a robotic arm, instrument driver 62 comprises of one ormore drive units 63 arranged with parallel axes to provide controlledtorque to a medical instrument via drive shafts 64. Each drive unit 63comprises an individual drive shaft 64 for interacting with theinstrument, a gear head 65 for converting the motor shaft rotation to adesired torque, a motor 66 for generating the drive torque, an encoder67 to measure the speed of the motor shaft and provide feedback to thecontrol circuitry, and control circuity 68 for receiving control signalsand actuating the drive unit. Each drive unit 63 being independentcontrolled and motorized, the instrument driver 62 may provide multiple(four as shown in FIG. 12) independent drive outputs to the medicalinstrument. In operation, the control circuitry 68 would receive acontrol signal, transmit a motor signal to the motor 66, compare theresulting motor speed as measured by the encoder 67 with the desiredspeed, and modulate the motor signal to generate the desired torque.

For procedures that require a sterile environment, the robotic systemmay incorporate a drive interface, such as a sterile adapter connectedto a sterile drape, that sits between the instrument driver and themedical instrument. The chief purpose of the sterile adapter is totransfer angular motion from the drive shafts of the instrument driverto the drive inputs of the instrument while maintaining physicalseparation, and thus sterility, between the drive shafts and driveinputs. Accordingly, an example sterile adapter may comprise of a seriesof rotational inputs and outputs intended to be mated with the driveshafts of the instrument driver and drive inputs on the instrument.Connected to the sterile adapter, the sterile drape, comprised of athin, flexible material such as transparent or translucent plastic, isdesigned to cover the capital equipment, such as the instrument driver,robotic arm, and cart (in a cart-based system) or table (in atable-based system). Use of the drape would allow the capital equipmentto be positioned proximate to the patient while still being located inan area not requiring sterilization (i.e., non-sterile field). On theother side of the sterile drape, the medical instrument may interfacewith the patient in an area requiring sterilization (i.e., sterilefield).

D. Medical Instrument.

FIG. 13 illustrates an example medical instrument with a pairedinstrument driver. Like other instruments designed for use with arobotic system, medical instrument 70 comprises an elongated shaft 71(or elongate body) and an instrument base 72. The instrument base 72,also referred to as an “instrument handle” due to its intended designfor manual interaction by the physician, may generally compriserotatable drive inputs 73, e.g., receptacles, pulleys or spools, thatare designed to be mated with drive outputs 74 that extend through adrive interface on instrument driver 75 at the distal end of robotic arm76. When physically connected, latched, and/or coupled, the mated driveinputs 73 of instrument base 72 may share axes of rotation with thedrive outputs 74 in the instrument driver 75 to allow the transfer oftorque from drive outputs 74 to drive inputs 73. In some embodiments,the drive outputs 74 may comprise splines that are designed to mate withreceptacles on the drive inputs 73.

The elongated shaft 71 is designed to be delivered through either ananatomical opening or lumen, e.g., as in endoscopy, or a minimallyinvasive incision, e.g., as in laparoscopy. The elongated shaft 66 maybe either flexible (e.g., having properties similar to an endoscope) orrigid (e.g., having properties similar to a laparoscope) or contain acustomized combination of both flexible and rigid portions. Whendesigned for laparoscopy, the distal end of a rigid elongated shaft maybe connected to an end effector comprising a jointed wrist formed from aclevis with an axis of rotation and a surgical tool, such as, forexample, a grasper or scissors, that may be actuated based on force fromthe tendons as the drive inputs rotate in response to torque receivedfrom the drive outputs 74 of the instrument driver 75. When designed forendoscopy, the distal end of a flexible elongated shaft may include asteerable or controllable bending section that may be articulated andbent based on torque received from the drive outputs 74 of theinstrument driver 75.

Torque from the instrument driver 75 is transmitted down the elongatedshaft 71 using tendons within the shaft 71. These individual tendons,such as pull wires, may be individually anchored to individual driveinputs 73 within the instrument handle 72. From the handle 72, thetendons are directed down one or more pull lumens within the elongatedshaft 71 and anchored at the distal portion of the elongated shaft 71.In laparoscopy, these tendons may be coupled to a distally mounted endeffector, such as a wrist, grasper, or scissor. Under such anarrangement, torque exerted on drive inputs 73 would transfer tension tothe tendon, thereby causing the end effector to actuate in some way. Inlaparoscopy, the tendon may cause a joint to rotate about an axis,thereby causing the end effector to move in one direction or another.Alternatively, the tendon may be connected to one or more jaws of agrasper at distal end of the elongated shaft 71, where tension from thetendon cause the grasper to close.

In endoscopy, the tendons may be coupled to a bending or articulatingsection positioned along the elongated shaft 71 (e.g., at the distalend) via adhesive, control ring, or other mechanical fixation. Whenfixedly attached to the distal end of a bending section, torque exertedon drive inputs 73 would be transmitted down the tendons, causing thesofter, bending section (sometimes referred to as the articulablesection or region) to bend or articulate. Along the non-bendingsections, it may be advantageous to spiral or helix the individual pulllumens that direct the individual tendons along (or inside) the walls ofthe endoscope shaft to balance the radial forces that result fromtension in the pull wires. The angle of the spiraling and/or spacingthere between may be altered or engineered for specific purposes,wherein tighter spiraling exhibits lesser shaft compression under loadforces, while lower amounts of spiraling results in greater shaftcompression under load forces, but also exhibits limits bending. On theother end of the spectrum, the pull lumens may be directed parallel tothe longitudinal axis of the elongated shaft 71 to allow for controlledarticulation in the desired bending or articulable sections.

In endoscopy, the elongated shaft 71 houses a number of components toassist with the robotic procedure. The shaft may comprise of a workingchannel for deploying surgical tools, irrigation, and/or aspiration tothe operative region at the distal end of the shaft 71. The shaft 71 mayalso accommodate wires and/or optical fibers to transfer signals to/froman optical assembly at the distal tip, which may include of an opticalcamera. The shaft 71 may also accommodate optical fibers to carry lightfrom proximally-located light sources, such as light emitting diodes, tothe distal end of the shaft.

At the distal end of the instrument 70, the distal tip may also comprisethe opening of a working channel for delivering tools for diagnosticand/or therapy, irrigation, and aspiration to an operative site. Thedistal tip may also include a port for a camera, such as a fiberscope ora digital camera, to capture images of an internal anatomical space.Relatedly, the distal tip may also include ports for light sources forilluminating the anatomical space when using the camera.

In the example of FIG. 13, the drive shaft axes, and thus the driveinput axes, are orthogonal to the axis of the elongated shaft. Thisarrangement, however, complicates roll capabilities for the elongatedshaft 71. Rolling the elongated shaft 71 along its axis while keepingthe drive inputs 73 static results in undesirable tangling of thetendons as they extend off the drive inputs 73 and enter pull lumenswithin the elongate shaft 71. The resulting entanglement of such tendonsmay disrupt any control methods intended to predict movement of theflexible elongate shaft during an endoscopic procedure.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument. As shown, a circular instrumentdriver 80 comprises four drive units with their drive outputs 81 alignedin parallel at the end of a robotic arm 82. The drive units, and theirrespective drive outputs 81, are housed in a rotational assembly 83 ofthe instrument driver 80 that is driven by one of the drive units withinthe assembly 83. In response to torque provided by the rotational driveunit, the rotational assembly 83 rotates along a circular bearing thatconnects the rotational assembly 83 to the non-rotational portion 84 ofthe instrument driver. Power and controls signals may be communicatedfrom the non-rotational portion 84 of the instrument driver 80 to therotational assembly 83 through electrical contacts may be maintainedthrough rotation by a brushed slip ring connection (not shown). In otherembodiments, the rotational assembly 83 may be responsive to a separatedrive unit that is integrated into the non-rotatable portion 84, andthus not in parallel to the other drive units. The rotational mechanism83 allows the instrument driver 80 to rotate the drive units, and theirrespective drive outputs 81, as a single unit around an instrumentdriver axis 85.

Like earlier disclosed embodiments, an instrument 86 may comprise of anelongated shaft portion 88 and an instrument base 87 (shown with atransparent external skin for discussion purposes) comprising aplurality of drive inputs 89 (such as receptacles, pulleys, and spools)that are configured to receive the drive outputs 81 in the instrumentdriver 80. Unlike prior disclosed embodiments, instrument shaft 88extends from the center of instrument base 87 with an axis substantiallyparallel to the axes of the drive inputs 89, rather than orthogonal asin the design of FIG. 13.

When coupled to the rotational assembly 83 of the instrument driver 80,the medical instrument 86, comprising instrument base 87 and instrumentshaft 88, rotates in combination with the rotational assembly 83 aboutthe instrument driver axis 85. Since the instrument shaft 88 ispositioned at the center of instrument base 87, the instrument shaft 88is coaxial with instrument driver axis 85 when attached. Thus, rotationof the rotational assembly 83 causes the instrument shaft 88 to rotateabout its own longitudinal axis. Moreover, as the instrument base 87rotates with the instrument shaft 88, any tendons connected to the driveinputs 89 in the instrument base 87 are not tangled during rotation.Accordingly, the parallelism of the axes of the drive outputs 81, driveinputs 89, and instrument shaft 88 allows for the shaft rotation withouttangling any control tendons.

E. Navigation and Control.

Traditional endoscopy may involve the use of fluoroscopy (e.g., as maybe delivered through a C-arm) and other forms of radiation-based imagingmodalities to provide endoluminal guidance to an operator physician. Incontrast, the robotic systems contemplated by this disclosure canprovide for non-radiation-based navigational and localization means toreduce physician exposure to radiation and reduce the amount ofequipment within the operating room. As used herein, the term“localization” may refer to determining and/or monitoring the positionof objects in a reference coordinate system. Technologies such aspre-operative mapping, computer vision, real-time EM tracking, and robotcommand data may be used individually or in combination to achieve aradiation-free operating environment. In other cases, whereradiation-based imaging modalities are still used, the pre-operativemapping, computer vision, real-time EM tracking, and robot command datamay be used individually or in combination to improve upon theinformation obtained solely through radiation-based imaging modalities.

FIG. 15 is a block diagram illustrating a localization system 90 thatestimates a location of one or more elements of the robotic system, suchas the location of the instrument, in accordance to an exampleembodiment. The localization system 90 may be a set of one or morecomputer devices configured to execute one or more instructions. Thecomputer devices may be embodied by a processor (or processors) andcomputer-readable memory in one or more components discussed above. Byway of example and not limitation, the computer devices may be in thetower 30 shown in FIG. 1, the cart shown in FIGS. 1-4, the beds shown inFIGS. 5-10, etc.

As shown in FIG. 15, the localization system 90 may include alocalization module 95 that processes input data 91-94 to generatelocation data 96 for the distal tip of a medical instrument. Thelocation data 96 may be data or logic that represents a location and/ororientation of the distal end of the instrument relative to a frame ofreference. The frame of reference can be a frame of reference relativeto the anatomy of the patient or to a known object, such as an EM fieldgenerator (see discussion below for the EM field generator).

The various input data 91-94 are now described in greater detail.Pre-operative mapping may be accomplished through the use of thecollection of low dose CT scans. Pre-operative CT scans generatetwo-dimensional images, each representing a “slice” of a cutaway view ofthe patient's internal anatomy. When analyzed in the aggregate,image-based models for anatomical cavities, spaces and structures of thepatient's anatomy, such as a patient lung network, may be generated.Techniques such as center-line geometry may be determined andapproximated from the CT images to develop a three-dimensional volume ofthe patient's anatomy, referred to as preoperative model data 91. Theuse of center-line geometry is discussed in U.S. patent application Ser.No. 14/523,760, the contents of which are herein incorporated in itsentirety. Network topological models may also be derived from theCT-images, and are particularly appropriate for bronchoscopy.

In some embodiments, the instrument may be equipped with a camera toprovide vision data (or image data) 92. The localization module 95 mayprocess the vision data to enable one or more vision-based (orimage-based) location tracking modules or features. For example, thepreoperative model data may be used in conjunction with the vision data92 to enable computer vision-based tracking of the medical instrument(e.g., an endoscope or an instrument advance through a working channelof the endoscope). For example, using the preoperative model data 91,the robotic system may generate a library of expected endoscopic imagesfrom the model based on the expected path of travel of the endoscope,each image linked to a location within the model. Intra-operatively,this library may be referenced by the robotic system in order to comparereal-time images captured at the camera (e.g., a camera at a distal endof the endoscope) to those in the image library to assist localization.

Other computer vision-based tracking techniques use feature tracking todetermine motion of the camera, and thus the endoscope. Some feature ofthe localization module 95 may identify circular geometries in thepreoperative model data 91 that correspond to anatomical lumens andtrack the change of those geometries to determine which anatomical lumenwas selected, as well as the relative rotational and/or translationalmotion of the camera. Use of a topological map may further enhancevision-based methods or techniques.

Optical flow, another computer vision-based technique, may analyze thedisplacement and translation of image pixels in a video sequence in thevision data 92 to infer camera movement. Through the comparison ofmultiple frames over multiple iterations, movement and location of thecamera (and thus the endoscope) may be determined.

The localization module 95 may use real-time EM tracking to generate areal-time location of the endoscope in a global coordinate system thatmay be registered to the patient's anatomy, represented by thepreoperative model. In EM tracking, an EM sensor (or tracker) comprisingof one or more sensor coils embedded in one or more locations andorientations in a medical instrument (e.g., an endoscopic tool) measuresthe variation in the EM field created by one or more static EM fieldgenerators positioned at a known location. The location informationdetected by the EM sensors is stored as EM data 93. The EM fieldgenerator (or transmitter), may be placed close to the patient to createa low intensity magnetic field that the embedded sensor may detect. Themagnetic field induces small currents in the sensor coils of the EMsensor, which may be analyzed to determine the distance and anglebetween the EM sensor and the EM field generator. These distances andorientations may be intra-operatively “registered” to the patientanatomy (e.g., the preoperative model) in order to determine thegeometric transformation that aligns a single location in the coordinatesystem with a position in the pre-operative model of the patient'sanatomy. Once registered, an embedded EM tracker in one or morepositions of the medical instrument (e.g., the distal tip of anendoscope) may provide real-time indications of the progression of themedical instrument through the patient's anatomy.

Robotic command and kinematics data 94 may also be used by thelocalization module 95 to provide localization data 96 for the roboticsystem. Device pitch and yaw resulting from articulation commands may bedetermined during pre-operative calibration. Intra-operatively, thesecalibration measurements may be used in combination with known insertiondepth information to estimate the position of the instrument.Alternatively, these calculations may be analyzed in combination withEM, vision, and/or topological modeling to estimate the position of themedical instrument within the network.

As FIG. 15 shows, a number of other input data can be used by thelocalization module 95. For example, although not shown in FIG. 15, aninstrument utilizing shape-sensing fiber can provide shape data that thelocalization module 95 can use to determine the location and shape ofthe instrument.

The localization module 95 may use the input data 91-94 incombination(s). In some cases, such a combination may use aprobabilistic approach where the localization module 95 assigns aconfidence weight to the location determined from each of the input data91-94. Thus, where the EM data may not be reliable (as may be the casewhere there is EM interference) the confidence of the locationdetermined by the EM data 93 can be decrease and the localization module95 may rely more heavily on the vision data 92 and/or the roboticcommand and kinematics data 94.

As discussed above, the robotic systems discussed herein may be designedto incorporate a combination of one or more of the technologies above.The robotic system's computer-based control system, based in the tower,bed and/or cart, may store computer program instructions, for example,within a non-transitory computer-readable storage medium such as apersistent magnetic storage drive, solid state drive, or the like, that,upon execution, cause the system to receive and analyze sensor data anduser commands, generate control signals throughout the system, anddisplay the navigational and localization data, such as the position ofthe instrument within the global coordinate system, anatomical map, etc.

2. Navigation of Luminal Networks.

The various robotic systems discussed above can be employed to perform avariety of medical procedures, such as endoscopic and laparoscopicprocedures. During certain procedures, a medical instrument, such as arobotically-controlled medical instrument, is inserted into a patient'sbody. Within the patient's body, the instrument may be positioned withina luminal network of the patient. As used herein, the term luminalnetwork refers to any cavity structure within the body, whethercomprising a plurality of lumens or branches (e.g., a plurality ofbranched lumens, as in the lung or blood vessels) or a single lumen orbranch (e.g., within the gastrointestinal tract). During the procedure,the instrument may be moved (e.g., navigated, guided, driven, etc.)through the luminal network to one or more areas of interest. Movementof the instrument through the system may be aided by the navigation orlocalization system 90 discussed above, which can provide positionalinformation about the instrument to a physician controlling the roboticsystem.

FIG. 16 illustrates an example luminal network 130 of a patient. In theillustrated embodiment, the luminal network 130 is a bronchial networkof airways 150 (i.e., lumens, branches) of the patient's lung. Althoughthe illustrated luminal network 130 is a bronchial network of airwayswithin the patient's lung, this disclosure is not limited to only theillustrated example. The robotic systems and methods described hereinmay be used to navigate any type of luminal network, such as bronchialnetworks, renal networks, cardiovascular networks (e.g., arteries andveins), gastrointestinal tracts, urinary tracts, etc.

As illustrated, the luminal network 130 comprises a plurality of lumens150 that are arranged in a branched structure. In general, the luminalnetwork 130 comprises a three-dimensional structure. For ease ofillustration, FIG. 16 represents the luminal network 130 as atwo-dimensional structure. This should not be construed to limit thepresent disclosure to two-dimensional luminal networks in any way.

FIG. 16 also illustrates an example of a medical instrument positionedwithin the luminal network 130. The medical instrument is navigatedthrough the luminal network 130 towards an area of interest (e.g.,nodule 155) for diagnosis and/or treatment. In the illustrated example,the nodule 155 is located at the periphery of the airways 150, althoughthe area(s) of interest can be positioned anywhere within the luminalnetwork 130 depending on the patient and procedure.

In the illustrated example, the medical instrument comprises anendoscope 115. The endoscope 115 can include a sheath 120 and a leader145. In some embodiments, the sheath 120 and the leader 145 may bearranged in a telescopic manner. For example, the leader 145 may beslidably positioned inside a working channel of the sheath 120. Thesheath 120 may have a first diameter, and its distal end may not be ableto be positioned through the smaller-diameter airways 150 around thenodule 155. Accordingly, the leader 145 may be configured to extend fromthe working channel of the sheath 120 the remaining distance to thenodule 155. The leader 145 may have a lumen through which instruments,for example biopsy needles, cytology brushes, and/or tissue samplingforceps, can be passed to the target tissue site of the nodule 155. Insuch implementations, both the distal end of the sheath 120 and thedistal end of the leader 145 can be provided with EM instrument sensors(e.g., EM instrument sensors 305 in FIG. 18) for tracking their positionwithin the airways 150. This telescopic arrangement of the sheath 120and the leader 145 may allow for a thinner design of the endoscope 115and may improve a bend radius of the endoscope 115 while providing astructural support via the sheath 120.

In other embodiments, the overall diameter of the endoscope 115 may besmall enough to reach the periphery without the telescopic arrangement,or may be small enough to get close to the periphery (e.g., within 2.5-3cm) to deploy medical instruments through a non-steerable catheter. Themedical instruments deployed through the endoscope 115 may be equippedwith EM instrument sensors (e.g., EM instrument sensors 305 in FIG. 18),and the image-based branch detection and mapping navigation techniquesdescribed below can be applied to such medical instruments.

As shown, to reach the nodule 155, the instrument (e.g., endoscope) mustbe navigated or guided through the lumens or branches 150 of the luminalnetwork. An operator (such as a physician) can control the roboticsystem to navigate the instrument to the nodule 155. The operator mayprovide inputs for controlling the robotic system.

FIG. 17 illustrates an example command console 200 that can be used withsome implementations of the robotic systems described herein. Theoperator may provide the inputs for controlling the robotic system, forexample, to navigate or guide the instrument to an area of interest suchas nodule 155, via the command console 200. The command console 200 maybe embodied in a wide variety of arrangements or configurations. In theillustrated example, the command console 200 includes a console base201, displays 202 (e.g., monitors), and one or more control modules(e.g., keyboard 203 and joystick 204). A user 205 (e.g., the operator orphysician) can remotely control the medical robotic system (e.g., thesystems described with reference to FIGS. 1-15) from an ergonomicposition using the command console 200.

The displays 202 may include electronic monitors (e.g., LCD displays,LED displays, touch-sensitive displays), virtual reality viewing devices(e.g., goggles or glasses), and/or other display devices. In someembodiments, one or more of the displays 202 displays positioninformation about the instrument, for example, as determined by thelocalization system 90 (FIG. 15). In some embodiments, one or more ofthe displays 202 displays a preoperative model of the patient's luminalnetwork 130. The positional information can be overlaid on thepreoperative model. The displays 202 can also display image informationreceived from a camera or another sensing device positioned on theinstrument within the luminal network 130. In some embodiments, a modelor representation of the instrument is displayed with the preoperativemodel to help indicate a status of a surgical or medical procedure.

In some embodiments, the console base 201 includes a central processingunit (CPU or processor), a memory unit (computer-readable memory), adata bus, and associated data communication ports that are responsiblefor interpreting and processing signals such as camera imagery andtracking sensor data, e.g., from a medical instrument positioned withina luminal network of a patient.

The console base 201 may also process commands and instructions providedby the user 205 through control modules 203, 204. In addition to thekeyboard 203 and joystick 204 shown in FIG. 20, the control modules mayinclude other devices, such as computer mice, trackpads, trackballs,control pads, controllers such as handheld remote controllers, andsensors (e.g., motion sensors or cameras) that capture hand gestures andfinger gestures. A controller can include a set of user inputs (e.g.,buttons, joysticks, directional pads, etc.) mapped to an operation ofthe instrument (e.g., articulation, driving, water irrigation, etc.).Using the control modules 203, 204 of the console base 200, the user 205may navigate an instrument through the luminal network 130.

FIG. 18 illustrates a detail view of a distal end of an example medicalinstrument 300. The medical instrument 300 of FIG. 18 may berepresentative of the endoscope 115 or steerable catheter 145 of FIG.16. The medical instrument 300 may be representative of any medicalinstrument described throughout the disclosure, such as the endoscope 13of FIG. 1, the ureteroscope 32 of FIG. 3, the laparoscope 59 of FIG. 9,etc. In FIG. 18, the distal end of the instrument 300 includes animaging device 315, illumination sources 310, and ends of EM sensorcoils 305, which form an EM instrument sensor. The distal end furtherincludes an opening to a working channel 320 of the instrument 300through which surgical instruments, such as biopsy needles, cytologybrushes, forceps, etc., may be inserted along the instrument shaft,allowing access to the area near the instrument tip.

EM coils 305 located on the distal end of the instrument 300 may be usedwith an EM tracking system to detect the position and orientation of thedistal end of the instrument 300 while it is positioned within a luminalnetwork. In some embodiments, the coils 305 may be angled to providesensitivity to EM fields along different axes, giving the disclosednavigational systems the ability to measure a full 6 degrees of freedom(DoF): three positional DoF and three angular DoF. In other embodiments,only a single coil 305 may be disposed on or within the distal end withits axis oriented along the instrument shaft. Due to the rotationalsymmetry of such a system, it may be insensitive to roll about its axis,so only five degrees of freedom may be detected in such animplementation. Alternatively or additionally, other types of positionsensors may be employed.

The illumination sources 310 provide light to illuminate a portion of ananatomical space. The illumination sources can each be one or morelight-emitting devices configured to emit light at a selected wavelengthor range of wavelengths. The wavelengths can be any suitable wavelength,for example, visible spectrum light, infrared light, x-ray (e.g., forfluoroscopy), to name a few examples. In some embodiments, illuminationsources 310 can include light-emitting diodes (LEDs) located at thedistal end of the instrument 300. In some embodiments, illuminationsources 310 can include one or more fiber optic fibers extending througha length of the endoscope to transmit light through the distal end froma remote light source, for example, an x-ray generator. Where the distalend includes multiple illumination sources 310 these can each beconfigured to emit the same or different wavelengths of light as oneanother.

The imaging device 315 can include any photosensitive substrate orstructure configured to convert energy representing received light intoelectric signals, for example, a charge-coupled device (CCD) orcomplementary metal-oxide semiconductor (CMOS) image sensor. Someexamples of imaging device 315 can include one or more optical fibers,for example, a fiber optic bundle, configured to transmit lightrepresenting an image from the distal end 300 of the endoscope to aneyepiece and/or image sensor near the proximal end of the endoscope.Imaging device 315 can additionally include one or more lenses and/orwavelength pass or cutoff filters as required for various opticaldesigns. The light emitted from the illumination sources 310 allows theimaging device 315 to capture images of the interior of a patient'sluminal network. These images can then be transmitted as individualframes or series of successive frames (e.g., a video) to a computersystem such as command console 200. As mentioned above and as will bedescribed in greater detail below, the images captured by the imagingdevice 315 (e.g., vision data 92 of FIG. 15) can be utilized by thenavigation or localization system 95 to determine or estimate theposition of the instrument (e.g., the position of the distal tip of theinstrument 300) within a luminal network.

3. Image-Based Branch Detection and Mapping for Navigation.

Embodiments of the disclosure relate to systems and techniques forimage-based branch detection and mapping. As used herein, image-basedbranch detection may refer to identifying within an image one or moreopenings associated with one or more branches of a luminal network. Forexample, an image-based branch detection system may capture an image ofan interior of a luminal network using an imaging device positioned onan instrument within the luminal network, and the image-based branchdetection system may analyze the to detect one or more openingsassociated with subsequent branches of the luminal network. As usedherein, image-based branch mapping may refer to mapping the detected oneor more openings to corresponding branches of the luminal network. Forexample, an image-based branch mapping system may be configured toidentify which one or more branches of a luminal network correspond tothe one or more detected openings within the image. These systems andtechniques may be used to determine or estimate the position of aninstrument within the luminal network. In certain implementations, thesesystems and techniques may be used in conjunction with various othernavigation and localization modalities (e.g., as described above withreference to FIG. 15).

A. Overview of Image-Based Branch Detection and Mapping for Navigation.

The ability to navigate inside a luminal network may be a feature of therobotically-controlled surgical systems described herein. As usedherein, navigation may refer to locating or determining the position ofan instrument within a luminal network. The determined position may beused to help guide the instrument to one or more particular areas ofinterest within the luminal network. In some embodiments, therobotically-controlled surgical systems utilize one or more independentsensing modalities to provide intra-operative navigation for theinstrument. As shown in FIG. 15, the independent sensing modalities mayposition data (e.g., EM data 93), vision data 92, and/or robotic commandand kinematics data 94. These independent sensing modalities may includeestimation modules configured to provide independent estimates ofposition. The independent estimates can then be combined into onenavigation output, for example, using localization module 95, which canbe used by the system or displayed to the user. Image-based branchdetection and mapping may provide an independent sensing modality, basedon vision data 92, that can provide an independent estimate of position.In particular, in some instances image-based branch detection andmapping provides a combination of a sensing modality and astate/position estimation module that estimates which lumen or branch ofa luminal network an imaging device of the instrument is in based on animage or images captured by the imaging device. In some embodiments, theestimate provided by image-based branch detection and mapping may beused alone or with other position estimates to determine a finalposition estimate that can be used by the system or displayed to theuser.

In some embodiments, there can be multiple state estimation modules thatwork in parallel based on the same sensing modality. As one example,there can be multiple (e.g., three) different state estimation modulesthat process vision data 92, each in different ways to output a multiple(e.g., three) different position estimates (all based on vision data92). This disclosure refers to one such module—an image-based branchdetection and mapping module—that detects branch openings based onvision data 92 (e.g., based on a single image) and estimates the currentposition of the instrument by mapping those detected branch openings tospecific anatomical branches in the luminal network. As will bedescribed in greater detail below, in some embodiments, the image-basedbranch detection and mapping module may use a current or previousposition estimate determined by navigation or localization system 90(that can be based on one or a plurality of sensing modalities) to mapthe detected openings to the specific anatomical branches in the luminalnetwork. Stated another way, the image-based branch detection andmapping systems and methods described herein may be configured toprovide a position estimate to the navigation or localization module 95of where the instrument is positioned in the luminal network. In someembodiments, the image-based branch detection and mapping systems andmethods described herein may be independent of any other modality. Insome embodiments, the image-based branch detection and mapping systemsand methods described herein may base its estimate on prior positionestimates determined using a plurality sensing modalities.

FIG. 19 illustrates an example method 400 for image-based branchdetection and mapping. The method 400 may be implemented in variousrobotically-controlled surgical systems as described herein. The method400 may include two steps or blocks: detecting branch openings in animage (block 402) and mapping the detected openings to branches of theluminal network (block 404).

At block 402, the method 400 detects branch openings within an image. Asnoted above, during a medical procedure, an instrument may be positionedwithin a luminal network (see FIG. 16). As shown in FIG. 18, theinstrument may include an imaging device 315 (such as a camera)positioned thereon. The imaging device 315 may capture images of theinterior of the luminal network. For example, at a particular instant,the imaging device 315 may capture an image of the interior of theparticular branch of the luminal network in which the instrument iscurrently positioned. At block 402, the method 400 can analyze the imageto detect one or more openings within the image. The one or moreopenings may connect one or more subsequent branches of the luminalnetwork to the current branch in which the instrument is positioned. Ingeneral terms, block 402 may involve image analysis that processes animage to determine whether the image contains one or more branchopenings. In certain implementations, if the image is determined tocontain one or more branch openings, various features of the openingsmay also be determined. Such features may include identifying a centroidof the detected one or more branch openings and/or identifying a shapeor contour of the detected one or more branch openings. Block 402(detection of branch openings in an image) may be referred to herein asimage-based branch detection, and is described in greater detail insection 3.B below.

At block 404, the method 400 maps the one or more detected branchopenings to specific branches of the luminal network. In general terms,at block 404, the method 400 determines which branches of the luminalnetwork are associated with the detected openings. In certainimplementations, block 404 may include determining a set of expectedsubsequent branches (for example, based on a current position estimateand a preoperative model of the luminal network) and matching featuresof the expected subsequent branches to the detected branch openings.Block 404 (mapping detected openings to branches of the luminal network)may be referred to herein as image-based branch mapping, and isdescribed in greater detail in section 3.0 below.

By mapping the detected openings to specific branches of the luminalnetwork, the method 400 may provide an estimate of position for theinstrument. For example, using the method 400, the system or theinstrument can identify which branches the instrument “sees” and usethis information to estimate where the instrument is within the luminalnetwork.

B. Image-Based Branch Detection.

Image-based branch detection may analyze an image captured by theimaging device 315 of an instrument positioned within a luminal networkto detect one or more branch openings in the image. For example,image-based branch detection analyzes an image of an interior of abranch to detect whether one or more openings connected subsequentbranches of the luminal network to the current branch are present in theimage.

FIG. 20 provides an example image 500 of an interior of a branch of aluminal network. In the illustrated example, the image 500 is aninterior image of an airway of a lung, although the image 500 may berepresentative of any type of luminal network. Two branch openings 502are present in the image 500. The branch openings 502 connect subsequentbranches (e.g., subsequent airways) to the current branch.

Image-based branch detection can include a method whereby a computersystem can recognize the branch openings 502 computationally. In somecases, the image 500 includes two classes of pixels: (1) pixelsrepresenting walls of the luminal network (e.g., tissue), and (2) pixelsrepresenting openings. According to certain embodiments, the image-basedbranch detection can systematically detect these two classes of pixelsto identify and detect branch openings within an image.

FIG. 21 illustrates an example method 600 for image-based branchdetection. The method 600 begins at block 602, where an image iscaptured or received. The image may be an image of an interior of abranch of a luminal network. The image may be captured by or receivedfrom an imaging device 315 on an instrument positioned within theluminal network as described above.

At block 604, a histogram of the image is generated. The histogram maybe a histogram of pixel intensity values, for example, plotting thenumber of pixels at each intensity value. Pixel intensity may rangebetween, for example, dark and light. A dark/light scale may berepresented, for example, numerically as a range, for example, between 0and 1 (with 0 representing totally dark (black) and 1 representingtotally light (white)), or between 0 and 256 (with 0 representingtotally dark (black) and 1 representing totally light (white)). Otherscales are also possible. Although his disclosure refers to an exampleof generating a histogram based on pixel intensity (brightness), thehistogram could also be generated based on other characteristics of theimage (such as color).

FIG. 22 illustrates an example histogram 700 of an image containing oneor more branch openings (e.g., image 500 of FIG. 20). In FIG. 22, thepixel intensity has been equalized and represented on a numerical scalebetween 0 and 256. Equalization of the histogram may result in a linearhistogram The bars represent the number of pixels in the image at eachintensity value. As shown, the histogram 700 is bimodal. That is, thehistogram 700 includes two distinct peaks 702, 704. The first peak 702may be representative of the pixels representing walls of the luminalnetwork (e.g., tissue), and the second peak 704 may be representative ofthe pixels representing openings within the image. In many instances, ahistogram of an interior of a branch of luminal network will be bimodal,including two peaks as shown. This may be because, in a tunnel like view(such as within a luminal network), pixels will generally either be dark(representing openings) or light (representing branch walls).

Returning to the method 600 of FIG. 21, at block 606, pixels above orbelow a threshold value are identified or categorized as indicatingbranch openings. Alternatively or additionally, pixels above or below athreshold value may be identified or categorized as indicating tissue orwalls of the luminal network. In general terms, at block 606, athreshold value is determined that divides the pixels of the imagebetween pixels representing branch openings and pixels representingbranch walls. By assigning or identifying pixels as either representingbranch openings or branch walls, branch openings within the image may bedetected.

FIGS. 23A and 23B illustrate example images 800 a, 800 b that show howbranch openings 802 can be detected. With reference to FIG. 23A, pixelsat the determined threshold value are highlighted, producing profiles805 surrounding the openings 802. With reference to FIG. 23B, pixelshave been segmented above and below the threshold value to identify theopenings 802. For example, all pixels above the threshold value havebeen segmented and illustrated in white, while all pixels below thethreshold value have been segmented and illustrate din black. In theexample of FIG. 23B, the black pixels represent the openings 802.

FIG. 24 illustrates an example subroutine or method 900 that can beimplemented in some embodiments of block 606 of the method 600 (FIG. 21)to identify/categorize pixels above/below a threshold value asindicating branch openings. The method 900 can include four steps orblocks. At block 902, peaks in the histogram are identified. As notedabove, in general, the histogram of an image of an interior of a lumenmay be bimodal, containing two identifiable peaks. For example, in thehistogram 700 of FIG. 22, a first peak 702 occurs at an intensity valueof 60 and a second peak 702 occurs at an intensity value of 180.

At block 904, a midpoint value between the peaks is identified. Withcontinued reference to the example of FIG. 22, a midpoint 704 betweenpeaks 702 of the histogram 700 occurs at an intensity of 140.Systematically, the midpoint 704 can be determined by finding a valuebetween the two peaks 702 that divides (e.g., equally) the histogram700. Returning to FIG. 24, at block 906, the threshold value is setequal to the midpoint 704. Thus, any pixel above the midpoint 704 orthreshold can be determined to be tissue and any pixel less than themidpoint 704 or threshold can be determined to be an opening. As shownin FIG. 23A, pixels at the threshold can be highlighted to illustratethe profile 805 of the openings 802.

Finally, at block 906, pixels above/below the threshold value areidentified or categorized as indicating branch openings. As shown inFIG. 23B, the threshold value can be used to segment the image intolight and dark areas, by assigning pixels above the threshold value amaximum intensity (e.g., white) and pixels below the threshold value aminimum intensity (e.g., black). As such, the openings 802 can bedetected and visualized.

As described, image-based branch detection can be configured to analyzean image to detect branch openings. The image-based branch detectionmethods described herein can be employed in various embodiments ofrobotically-controlled surgical systems described throughout thisdisclosure. In some embodiments, image-based branch detection comprisesimplements a method for identifying openings of branches of a luminalnetwork. The method may include capturing an image of an interior abranch of a luminal network with an imaging device positioned within thebranch. The image may be captured using an imaging device 315 on aninstrument positioned within the branch of the luminal network. Themethod may also include generating a histogram of pixel intensity valuesfor the image. In general, the histogram may be bimodal with peaksoccurring representative of tissue (e.g., walls of the luminal networks)and branch openings. The method may also include identifying pixelsbelow a threshold value as indicating openings within the image.

In some embodiments, the method also includes determining the thresholdvalue based on the histogram. The threshold value may be the midpointvalue between the two peaks of the histogram. For example, determiningthe threshold value may include identifying at least two peaks withinthe histogram, identifying a midpoint between the at least two peaks,and setting the threshold value equal to the intensity value of themidpoint. In other embodiments, the threshold value may be determined byother methods. For example, the threshold value may be a predeterminedvalue stored in a memory.

The image-based branch detection methods may include various otherfeatures. For example, in some embodiments, the image-based branchdetection methods may include identifying other features of the detectedbranch openings. For example, an image-based branch detection method mayalso include, for each of the identified openings within the image,determining a centroid of the opening. As another example, animage-based branch detection method may also include, for each of theidentified openings within the image, determine a profile of theopening. The profile may be determined by identifying pixels at thethreshold value.

In some embodiments, an image-based branch detection method may alsoinclude comparing a number of the identified openings in the image to abad frame detector threshold. In some embodiments, the bad framedetector threshold is set at three, four, five, six, or more. If theimage-based branch detection method detects a number of openings greaterthan or equal to the bad frame detector value, the method may determinea bad frame or error and discard the image. For example, in someinstances, bubbles or other features within the image may appear asopenings and produce false positives. If the number of detected openingsexceeds a likely number of openings as represented by the bad framedetector threshold, the method may determine that it has identifiedfalse positives (e.g., openings that are not really openings). In such acase, the method may discard the current image, redetect openings withina second image. For example, if the number of the identified openingsexceeds the bad frame detector threshold, the method may furtherincludes capturing a second image of the interior of the branch, andanalyzing the second image to determine openings within the secondimage.

C. Image-Based Branch Mapping

Image-based branch mapping determines or identifies which branches ofthe luminal network are associated with the detected openings. That is,image-based branch mapping can determine which subsequent branches ofthe luminal network are connected to the current branch at the detectedbranch openings. By mapping the detected openings to branches of theluminal network, the position of the instrument within the luminalnetwork can be determined. Further, an estimate or prediction of whichbranch the instrument will be moved into can also be obtained.

In broad terms, detected openings can be mapped to branches of theluminal network by comparing features of the detected openings tofeatures of the branches of the luminal network. The features of thedetected openings may be determined through image analysis as describedabove. The features of the branches of the luminal network can bedetermined from a model of the luminal network, such as a preoperativemodel of the luminal network. Further, in certain embodiments mappingdetected openings to branches of the luminal network can be based on acurrent position estimate of the instrument within the luminal network.The current position estimate can be determined based on various sensingmodalities as described above with reference to FIG. 15. Mappingdetected openings to branches of the luminal network based on a currentposition estimate can improve the efficiency, speed, and/or accuracy ofthe mapping process. For example, given a current position estimate,features of the detected openings can be compared to features ofexpected subsequent branches. This may minimize the computational loadrequired to perform the mapping and improve mapping speed.

FIG. 25 illustrates a simplified representation of a luminal network1000. The luminal network 1000 comprises a plurality of branches (e.g.,lumens, segments, etc.) 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016.The luminal network 1000 also comprises bifurcations 1020, 1022, 1024connecting various branches to each other. The luminal network 1000 mayrepresent a portion of a bronchial network of a lung, and the branchesmay represent airways. The luminal network 1000 may be represented by amodel. The model may be determined preoperatively. Preoperative modeldata 91 (i.e., information about the preoperative model) may be storedand made available to the navigation and localization system 90 (FIG.15). As will be described below as an example, image-based branchmapping can be configured to map the detected openings to the branchesof the luminal network 1000.

FIG. 26 illustrates an example method 1100 for image-based branchmapping. The method 1100 can be implemented by various of therobotically-controlled surgical systems described herein. The method1100 will be described by way of example, with reference to the luminalnetwork 1000 of FIG. 25, but is not limited thereto.

At block 1102, the method 1100 receives or determines a position stateestimate for an instrument positioned within the luminal network 1000.The position state estimate can include an identification of whichbranch the instrument is currently positioned. The position stateestimate can be determined, for example, by the navigation andlocalization system 90 of FIG. 15. The position state estimate can bedetermined based on various and/or multiple position sensing modalitiesand information, such as preoperative model data 91, vision data 92, EMdata 93 (or other position sensing data), and/or robotic command andkinematics data 94.

With reference to FIG. 25, for example, the position state estimate mayinclude an indication that the instrument is currently positioned withinany branch of the luminal network 1000 (e.g., branch 1002, branch 1004,branch 1006, etc.).

The position state estimate may also include additional information.Such additional information may include a determination or estimate ofdepth within the current segment and/or a determination or estimate ofcurrent instrument roll (e.g., rotation around a longitudinal axis ofthe instrument). In some embodiments, the system or method may maintainor generate multiple position state estimates and may assignprobabilities to each of the position state estimates. Of the multipleposition state estimates, the user may be provided with the mostprobable position state estimate. For example, the system or method maygenerate a first position state estimate (comprising for example, anindication that the instrument is positioned within the first branch1002, at first depth and roll angle) and a second position stateestimate (comprising for example, an indication that the instrument ispositioned within the second branch 1004, at second depth and rollangle). The system may determine probabilities for each position stateestimate. For example, the system may determine that there is a 60%probability that the instrument is at the first position state estimateand a 40% probability that the instrument is at the second positionstate estimate. Because the probability of the first position stateestimate is higher, the system may provide the first position stateestimate to the user or use the first position state estimate in one ormore additional steps of the method 1100.

At block 1104, the method 1100 determines a set of expected subsequentbranches based on the position state estimate determined at block 1102.For example, if the position state estimate indicates that theinstrument is in branch 1002, the set of expected subsequent branchesmay include those branches that are connected to branch 1002: branch1004 and branch 1006. As another example, if the position state estimateindicates that the instrument is in branch 1004, the set of expectedsubsequent branches may include branch 1008 and branch 1010. As anotherexample, if the position state estimate indicates that the instrument isin branch 1006, the set of expected subsequent branches may includebranch 1012, branch 1014, and branch 1016. Information about the set ofsubsequent branches may be derived from the preoperative model stored aspreoperative model data 91 (FIG. 15).

In addition to an indication of the subsequent branches, additional dataabout the set of expected subsequent branches can also be determined.For example, centroids of the openings of the expected subsequentbranches and/or profiles of the openings of the set of subsequentbranches can also be determined from the preoperative model.

At block 1106, the method 1100 may perform a correction for instrumentroll. As the instrument navigates the luminal network 1000, theinstrument may experience roll (e.g., roll about its longitudinal axis).Such roll may be a commanded roll to facilitate movement through theluminal network or an unintended roll. Information about the roll of theinstrument can be determined from, for example, the robotic command andkinematics data 94 and/or physical properties of the instrument such astorsional stiffness, etc. In some instances, it may be necessary tocorrect for instrument roll so that features of the detected openingscan be compared to features of the set of expected subsequent openingsas described at block 1108 below. An example of blocks 1106, 1108, 1110is described below with reference to FIGS. 27A-27C.

At block 1108, the method 1100 compares features of the detectedopenings to openings of the set of detected subsequent branches asdetermined at block 1104. In one example, a vector connecting centroidsof the detected openings is compared to a vector connecting centroids ofthe openings of the set of expected subsequent openings. In anotherembodiment, a shape or profile for each detected opening is compared toa shape or profile for each opening of the set of detected subsequentopenings. Other features may also be compared.

At block 1110, the method 1100 maps the detected openings to branches ofthe luminal network 1000. Mapping may be based on the comparison ofblock 1108, with closest matches used to map the detected openings tothe branches of the luminal network 1000.

For example, FIG. 27A illustrates an image 1202 including two detectedopenings. Centroids 1204 for each detected opening have been identified,and a vector V_(i) connecting the centroids 1204 is determined. In theleft panel of FIG. 27B, a set of expected subsequent branches has beendetermined based on a current position estimate of the instrument. Theset of expected subsequent branches includes, in this example, branch 2and branch 119. A vector V_(M) connecting branch 119 to branch 2 isillustrated. As shown in the right panel of FIG. 27B, the vector V_(M)can be corrected for instrument roll to produce a vector V_(v). Althoughnot illustrated, a second vector connecting branch 2 to branch 119 canalso be determined and corrected for roll. This second vector will beequal in magnitude but opposite in direction to the vector V_(v). Thesetwo vectors can then be compared to the vector V_(i). In some instances,comparing these vectors comprising taking the dot product. The closestmatch (e.g., the dot product nearest to one, in some examples) can thenbe used to map the detected openings to the set of expected subsequentbranches. As illustrated in FIG. 27C, the two detected branches havebeen mapped to branch 2 and branch 119 as shown.

As another example, a method for image-based branch mapping can includethe following: (1) identifying the location (e.g., the x and ycoordinates) of the detected branches within an image; (2) determiningor receiving an estimate of which branch the instrument is currentlypositioned in; (3) using this estimate of the current branch, generatinga list of all direct children (i.e., branches connecting to the currentbranch) that exist for estimated branch, as well their positions (e.g.,their x and y coordinates) based on the preoperative model; (4)iteratively matching these children's transformed (e.g., roll corrected)coordinates to the locations determined at step 1 and computing a cost(metric) for each iteration (or pairs of iterations); and (5) using thelowest cost match to assign these children to detected branches.

D. Image-Based Branch Prediction

In certain implementations, the systems and methods of the presentdisclosure may also predict or estimate which airway the instrument islikely to enter next based on its current position. In someimplementations, the systems and methods of the present disclosure mayprovide predictions or estimates of probabilities for entering each ofthe detected and mapped branches. This may be accomplished, in certainexamples, by determining which of the detected and mapped branches isclosest to the center of the image.

FIG. 28 provides an image 1300 illustrating distances d₂ and d₁₁₉between the center of the image 1300 and the centroids of two detectedand mapped openings 2 and 119. As shown, the distance d₂ is less thanthe distance d₁₁₉ because the centroid of the opening 2 is closer to thecenter of the image. Accordingly, the methods or systems may provide anestimate or prediction that the instrument is likely to enter branch 2.In some instances, the methods or systems may provide probabilities forentering branch 2 and branch 119. The probabilities may beproportionally related to the distances d₂ and d₁₁₉. Shorter distancesmay relate to a higher probability. This may be because the shorterdistance may indicate that the instrument is facing or pointed towardthe corresponding opening.

These estimates or probabilities may be provided to the localizationmodule 95 (FIG. 15) and used to provide updated location data 96. Thus,in some embodiments, future position state estimates may advantageouslybe based, at least in part, on previously determined position stateestimates that can include probabilities of which of a plurality ofbranches the instrument is likely to enter. The system may determinethat the instrument is most likely to enter the opening that is closestto the center of the image. This may facilitate navigation as futureposition state estimates account for probabilities determined atprevious position state estimates. In some instances, this mayadvantageously reduce a computational load required to estimateposition. In some embodiments, this may advantageously decrease the timerequired to determine a position state estimate. In some embodiments,this may improve the accuracy of a position state estimate.

E. Example Image-Based Branch Detection and Mapping Navigation Methodsand Systems

FIG. 29 illustrates an example method 1400 for implementing theimage-based branch detection and mapping as described above. The method1400 can be implemented in various of the robotically-controlled systemsdescribed throughout this disclosure. The method 1400 can be implementedwith a robotic system including an instrument having an elongate bodyconfigured to be inserted into a luminal network. An imaging device canbe positioned on the elongate body (e.g., on a distal end of theelongate body). The instrument can be attached to an instrumentpositioning device (e.g., a robotic arm) configured to move theinstrument through the luminal network. A system employing the method1400 can include a processor configured with instructions that cause aprocessor to execute the method 1400. The method 1400 is provided by wayof example only and the image-based branch detection and mapping can beimplemented using different steps than those shown in FIG. 29.

At block 1402, the method 1400 receives or determines a position stateestimate. In some embodiments, block 1402 determines the position stateestimate of the instrument positioned within a current branch of theluminal network. The position state estimate may be determined based onone or more of various sensing modalities by the navigation andlocalization system 90 of FIG. 15.

At block 1404, the method 1400 determines a set of expected subsequentbranches. In some embodiments, block 1404 determines the set of expectedsubsequent branches based at least in part on the initial state estimateand a preoperative model of the luminal network.

At block 1406, the method 1400 captures an image of the current branch.In some embodiments, block 1406 captures the image of the current branchof the luminal network with an imaging device positioned on theinstrument (e.g., imaging device 315).

At block 1408, the method 1400 analyzes the image to detect openingswithin the image. In some embodiments, block 1408 detects within theimage a plurality of openings connecting subsequent branches of theluminal network to the current branch. In some embodiments, detectingthe plurality of openings within the image includes performing imageanalysis. In some embodiments, the image analysis includes generating ahistogram of pixel intensity values for the image and analyzing thehistogram to identify the plurality of openings within the image. Insome embodiments, analyzing the histogram includes identifying at leasttwo peaks within the histogram, identifying a midpoint between the atleast two peaks, and categorizing pixels on a first side of the midpointas openings.

In some embodiments, at block 1408, the method 1400 also determines oneor more features of the detected openings. The one or more features maybe selected from the group consisting of: a centroid of an opening, aprofile of an opening, and a vector connecting centroids of twoopenings.

At block 1410, the method 1400 compares features of the detectedopenings to the set of expected subsequent branches. In someembodiments, block 1410 compares features of the detected plurality ofopenings to the set of expected subsequent branches to map each of theplurality of openings to one of the expected subsequent branches. Insome embodiments, the method 1400 also includes obtaining informationrelated to the set of expected subsequent branches from the preoperativemodel. The information can include at least one of centroid of anopening, a profile of an opening, and a vector connecting centroids oftwo openings. In some embodiments, comparing features of the detectedplurality of openings to the set of expected subsequent branchesincludes, for each of the detected openings, iteratively matching theone or more features of the detected opening to the information relatedto the set of expected subsequent branches. In some embodiments, thehighest match is used to map the detected opening to the one of theexpected subsequent branches.

At block 1412, the method 1400, provides an updated position stateestimate. In some embodiments, based at least in part on the comparison,block 1412 provides an updated position state estimate. In someembodiments, the updated position state estimate includes a probabilitythat the position state estimate is correct. In some embodiments, theprobability is determined based in part on the comparison between theone or more features of the detected plurality of openings and the setof expected subsequent branches. In some embodiments, the probability isdetermined based in part on the degree to which the one or more featuresof the detected plurality of openings match the set of expectedsubsequent branches. In some embodiments, the updated position stateestimate includes an estimate of which subsequent branch the instrumentwill be moved into.

In some embodiments, the method 1400 further includes determining whichopening of the plurality of detected openings is closer to a center ofthe image. In some embodiments, the updated position state estimateincludes a probability that the instrument will be moved into theopening that is determined to be closer to the center of the image.

In some embodiments, the instrument comprises an endoscope. In someembodiments, the luminal network comprises a bronchial network of alung, a gastrointestinal tract, or a renal network of a kidney, althoughnavigation of other luminal networks is also possible.

4. Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods and apparatusfor image-based branch detection and mapping for navigationrobotically-controlled medical instruments. Various implementationsdescribed herein provide for improved navigation of luminal networks.

It should be noted that the terms “couple,” “coupling,” “coupled” orother variations of the word couple as used herein may indicate eitheran indirect connection or a direct connection. For example, if a firstcomponent is “coupled” to a second component, the first component may beeither indirectly connected to the second component via anothercomponent or directly connected to the second component.

The position estimation and robotic motion actuation functions describedherein may be stored as one or more instructions on a processor-readableor computer-readable medium. The term “computer-readable medium” refersto any available medium that can be accessed by a computer or processor.By way of example, and not limitation, such a medium may comprise randomaccess memory (RAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory, compact discread-only memory (CD-ROM) or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store desired program code in the form of instructions ordata structures and that can be accessed by a computer. It should benoted that a computer-readable medium may be tangible andnon-transitory. As used herein, the term “code” may refer to software,instructions, code or data that is/are executable by a computing deviceor processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the scope of the invention. For example, it will be appreciatedthat one of ordinary skill in the art will be able to employ a numbercorresponding alternative and equivalent structural details, such asequivalent ways of fastening, mounting, coupling, or engaging toolcomponents, equivalent mechanisms for producing particular actuationmotions, and equivalent mechanisms for delivering electrical energy.Thus, the present invention is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A non-transitory computer readable storage mediumhaving stored thereon instructions that, when executed, cause aprocessor of a device to at least: determine a position state estimateof an instrument positioned within a current branch of a luminalnetwork, the instrument having an elongate body configured to beinserted into the luminal network and an imaging device positioned on adistal portion of the elongate body, the instrument attached to aninstrument positioning device configured to move the instrument throughthe luminal network; determine a set of expected subsequent branchesbased at least in part on the position state estimate and a preoperativemodel of the luminal network; capture an image of the current branchwith the imaging device positioned on the instrument; detect within theimage a plurality of openings connecting subsequent branches of theluminal network to the current branch; determine an estimate of a rollof the instrument based on comparing an orientation of the detectedopenings within the image to an orientation of the set of expectedsubsequent branches; determine a first feature of the detected pluralityof openings; determine a second feature of the set of expectedsubsequent branches; calculate a roll correction value based on theestimate of the roll of the instrument; correct the second feature basedon the roll correction value; compare the first feature to the correctedsecond feature to determine a mapping of each of the plurality ofopenings to one of expected subsequent branches; and based at least inpart on the mapping, provide an updated position state estimate.
 2. Thenon-transitory computer readable storage medium of claim 1, wherein theupdated position state estimate comprises a probability that theposition state estimate is correct.
 3. The non-transitory computerreadable storage medium of claim 2, wherein the probability isdetermined based in part on the comparison between the first feature tothe corrected second feature.
 4. The non-transitory computer readablestorage medium of claim 1, wherein the updated position state estimatecomprises an estimate of which subsequent branch the instrument will bemoved into.
 5. The non-transitory computer readable storage medium ofclaim 1, wherein: the instructions, when executed, cause the processorof the device to determine which opening of the plurality of detectedopenings is closer to a center of the image, the updated position stateestimate comprises a probability that the instrument will be moved intothe opening that is determined to be closer to the center of the image.6. The non-transitory computer readable storage medium of claim 1,wherein the instructions, when executed, cause the processor of thedevice to determine the first feature of the detected openings selectedfrom the group consisting of: a centroid of an opening, a profile of anopening, and a vector connecting centroids of two openings.
 7. Thenon-transitory computer readable storage medium of claim 6, wherein theinstructions, when executed, cause the processor of the device todetermine the second feature of the set of expected subsequent branchesfrom the preoperative model, wherein the information comprises at leastone of centroid of an opening, a profile of an opening, and a vectorconnecting centroids of two openings.
 8. The non-transitory computerreadable storage medium of claim 7, wherein the instructions, whenexecuted, cause the processor of the device to compare one or morefeatures of the detected plurality of openings to the set of expectedsubsequent branches by: for each of the detected openings, iterativelymatching the first feature of the detected opening to the correctedsecond feature of the set of expected subsequent branches, wherein thehighest match is used to map the detected opening to the one of theexpected subsequent branches.
 9. A robotic system for navigating aluminal network of a patient, the robotic system comprising: aninstrument having an elongate body configured to be inserted into theluminal network, and an imaging device positioned on a distal portion ofthe elongate body; an instrument positioning device attached to theinstrument, the instrument positioning device configured to move theinstrument through the luminal network; at least one non-transitorycomputer-readable memory having stored thereon executable instructions;and one or more processors in communication with the at least onenon-transitory computer-readable memory and configured to execute theinstructions to cause the system to at least: determine a position stateestimate of the instrument positioned within a current branch of theluminal network; determine a set of expected subsequent branches basedat least in part on the initial state estimate and a preoperative modelof the luminal network; capture an image of the current branch of theluminal network with the imaging device positioned on the instrument;detect within the image a plurality of openings connecting subsequentbranches of the luminal network to the current branch; determine anestimate of a roll of the instrument based on comparing an orientationof the detected openings within the image to an orientation of the setof expected subsequent branches; determine a first feature of thedetected plurality of openings; determine a second feature of the set ofexpected subsequent branches; calculate a roll correction value based onthe estimate of the roll of the instrument; correct the second featurebased on the roll correction value; compare the first feature to thecorrected second feature to determine a mapping of each of the pluralityof openings to one of the expected subsequent branches; and based atleast in part on the mapping, provide an updated position stateestimate.
 10. The system of claim 9, wherein the instrument comprises anendoscope.
 11. The system of claim 9, wherein the instrument positioningdevice comprises a robotic arm.
 12. The system of claim 9, wherein theluminal network comprises a bronchial network of a lung, agastrointestinal tract, or a renal network of a kidney.
 13. The systemof claim 9, wherein the instructions, when executed, cause the one ormore processors to determine which opening of the plurality of detectedopenings is closer to a center of the image, and wherein the updatedposition state estimate comprises a probability that the instrument willbe moved into the opening that is determined to be closer to the centerof the image.
 14. The system of claim 9, wherein the instructions, whenexecuted, cause the one or more processors to determine the firstfeature of the detected openings, wherein the first feature is selectedfrom the group consisting of: a centroid of an opening, a profile of anopening, and a vector connecting centroids of two openings.
 15. Thesystem of claim 14, wherein the instructions, when executed, cause theone or more processors of the device to determine the second feature ofthe set of expected subsequent branches from the preoperative model,wherein the second feature comprises at least one of centroid of anopening, a profile of an opening, and a vector connecting centroids oftwo openings.
 16. The system of claim 15, wherein the instructions, whenexecuted, cause the one or more processors to compare one or morefeatures of the detected plurality of openings to the set of expectedsubsequent branches by: for each of the detected openings, iterativelymatching the first feature of the detected opening to the correctedsecond feature of the set of expected subsequent branches, wherein thehighest match is used to map the detected opening to the one of theexpected subsequent branches.
 17. A method for navigating a luminalnetwork, the method comprising: inserting an instrument into a currentbranch of the luminal network, the instrument having an elongate bodyconfigured to be inserted into the luminal network and an imaging devicepositioned on a distal portion of the elongate body, the instrumentattached to an instrument positioning device configured to move theinstrument through the luminal network; receiving a position stateestimate for the instrument; determining a set of expected subsequentbranches based at least in part on the initial state estimate and apreoperative model of the luminal network; capturing an image of thecurrent branch with the imaging device positioned on the instrument;analyzing the image to detect a plurality of openings connectingsubsequent branches to the current branch; determining an estimate of aroll of the instrument based on comparing an orientation of the detectedopenings within the image to an orientation of the set of expectedsubsequent branches; determining a first feature of the detectedplurality of openings; determining a second feature of the set ofexpected subsequent branches; calculating a roll correction value basedon the estimate of the roll of the instrument; correcting the secondfeature based on the roll correction value; comparing the first featureto the corrected second feature to determine a mapping of each of theplurality of openings to one of the expected subsequent branches; andbased at least in part on the mapping, provide an updated position stateestimate.
 18. The method of claim 17, wherein the updated position stateestimate comprises a probability that the position state estimate iscorrect.
 19. The method of claim 18, wherein the probability isdetermined based in part on the comparison between the first feature tothe corrected second feature.
 20. The method of claim 19, wherein theprobability is determined based in part on the degree to which the oneor more features of the detected plurality of openings match the set ofexpected subsequent branches.
 21. The method of claim 17, furthercomprising: determining which opening of the plurality of detectedopenings is closer to a center of the image; and wherein the updatedposition state estimate comprises a probability that the instrument willbe moved into the opening that is determined to be closer to the centerof the image.
 22. The method of claim 17, further comprising:determining the first feature of the detected openings, wherein thefirst feature is selected from the group consisting of: a centroid of anopening, a profile of an opening, and a vector connecting centroids oftwo openings; and determining the second feature of the set of expectedsubsequent branches from the preoperative model, wherein the informationcomprises at least one of centroid of an opening, a profile of anopening, and a vector connecting centroids of two openings; and whereincomparing the first feature to the corrected second feature comprises:for each of the detected openings, iteratively matching the firstfeature of the detected opening to the corrected second feature of theset of expected subsequent branches, wherein the highest match is usedto map the detected opening to the one of the expected subsequentbranches.